Using the coq theorem prover to verify complex data structure invariants

2017 ◽  
Author(s):  
Kenneth Roe ◽  
Scott F. Smith
Keyword(s):  
Data Structure ◽  
Theorem Prover ◽  
Complex Data ◽  
Complex Data Structure

New approach to the statistical analysis of cardiovascular data

2005 ◽  
Vol 98 (6) ◽  
pp. 2298-2303 ◽  
Author(s):  
Michele R. Norton ◽  
Richard P. Sloan ◽  
Emilia Bagiella
Keyword(s):  
Blood Pressure ◽  
Data Structure ◽  
Repeated Measures ◽  
Blood Pressure Variability ◽  
Statistical Tests ◽  
Frequency Interval ◽  
Complex Data ◽  
Single Measure ◽  
New Approach ◽  
Complex Data Structure

Fourier-based approaches to analysis of variability of R-R intervals or blood pressure typically compute power in a given frequency band (e.g., 0.01–0.07 Hz) by aggregating the power at each constituent frequency within that band. This paper describes a new approach to the analysis of these data. We propose to partition the blood pressure variability spectrum into more narrow components by computing power in 0.01-Hz-wide bands. Therefore, instead of a single measure of variability in a specific frequency interval, we obtain several measurements. The approach generates a more complex data structure that requires a careful account of the nested repeated measures. We briefly describe a statistical methodology based on generalized estimating equations that suitably handles this more complex data structure. To illustrate the methods, we consider systolic blood pressure data collected during psychological and orthostatic challenge. We compare the results with those obtained using the conventional methods to compute blood pressure variability, and we show that our approach yields more efficient results and more powerful statistical tests. We conclude that this approach may allow a more thorough analysis of cardiovascular parameters that are measured under different experimental conditions, such as blood pressure or heart rate variability.

scholarly journals Construction of a Flexible Data Structures Laboratory

2007 ◽  
Vol 10 (1) ◽  
Author(s):  
Jorge Villalobos ◽  
Danilo Pérez ◽  
Juan Castro ◽  
Camilo Jiménez
Keyword(s):  
Data Structure ◽  
Computer Science ◽  
Data Structures ◽  
Efficient Algorithm ◽  
Science Curriculum ◽  
Problem Solution ◽  
Complex Data ◽  
Different Types ◽  
Computer Science Curriculum ◽  
Complex Data Structure

In a computer science curriculum, the data structures course is considered fundamental. In that course, students must generate the ability to desingn the more suitable data structures for a problem solution. They must also write an efficient algorithm in order to solve the problem. Students must understand that there are different types of data structures, each of them with associated algorithms of different complexity. A data structures laboratory is a set of computional tools that helps students in the experimentation with the concepts introduced in the curse. The main objetive of this experimentation is to generate the student's needed abilities for manipulating complex data structure. This paper presents the main characteristics of the laboratory built as a sopport of the course. we illustrate the huge possibilities of the tool with an example.

Efficient application-independent visualization engine for information with complex data structure

1997 ◽  
Author(s):  
Frank W. Tseng ◽  
Dikran S. Meliksetian ◽  
C. Y. Roger Chen
Keyword(s):  
Data Structure ◽  
Complex Data ◽  
Complex Data Structure

scholarly journals OCRbayes: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253926
Author(s):  
Xiang Zhang ◽  
Taolin Yuan ◽  
Jaap Keijer ◽  
Vincent C. J. de Boer
Keyword(s):  
Oxygen Consumption ◽  
Data Structure ◽  
Consumption Rate ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Complex Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Extracellular Flux ◽  
Complex Data Structure

Background Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. Results To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. Conclusions We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

scholarly journals OCRbayes: A Bayesian hierarchical modeling framework for Seahorse extracellular flux oxygen consumption rate data analysis

2021 ◽  
Author(s):  
Xiang Zhang ◽  
Taolin Yuan ◽  
Jaap Keijer ◽  
Vincent C. J. de Boer
Keyword(s):  
Oxygen Consumption ◽  
Data Structure ◽  
Consumption Rate ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Modeling ◽  
Complex Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Extracellular Flux ◽  
Complex Data Structure

Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

scholarly journals Corpona – The Pythonic Way of Processing Corpora

2021 ◽  
pp. 25-30
Author(s):  
Khalid Alnajjar ◽  
◽  
Mika Hämäläinen
Keyword(s):  
Data Structure ◽  
Open Source ◽  
Easy Access ◽  
Complex Data ◽  
Complex Data Structure

Every NLP researcher has to work with different XML or JSON encoded files. This often involves writing code that serves a very specific purpose. Corpona is meant to streamline any workflow that involves XML and JSON based corpora, by offering easy and reusable functionalities. The current functionalities relate to easy parsing and access to XML files, easy access to sub-items in a nested JSON structure and visualization of a complex data structure. Corpona is fully open-source and it is available on GitHub and Zenodo.

scholarly journals In-Memory Interval Joins

2021 ◽  
Author(s):  
Panagiotis Bouros ◽  
Nikos Mamoulis ◽  
Dimitrios Tsitsigkos ◽  
Manolis Terrovitis
Keyword(s):  
Parallel Computation ◽  
State Of The Art ◽  
Complex Data ◽  
Plane Sweep ◽  
Join Algorithm ◽  
Sweep Algorithm ◽  
Join Algorithms ◽  
Domain Partitioning ◽  
Complex Data Structure ◽  
Independent Tasks

AbstractThe interval join is a popular operation in temporal, spatial, and uncertain databases. The majority of interval join algorithms assume that input data reside on disk and so, their focus is to minimize the I/O accesses. Recently, an in-memory approach based on plane sweep (PS) for modern hardware was proposed which greatly outperforms previous work. However, this approach relies on a complex data structure and its parallelization has not been adequately studied. In this article, we investigate in-memory interval joins in two directions. First, we explore the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation. We propose four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art. Second, we study in depth the parallel computation of interval joins. We design a non-partitioning-based approach that determines independent tasks of the join algorithm to run in parallel. Then, we address the drawbacks of the previously proposed hash-based partitioning and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach, we propose a novel breakdown of the partition-joins into mini-joins to be scheduled in the available CPU threads and propose an adaptive domain partitioning, aiming at load balancing. We also investigate how the partitioning phase can benefit from modern parallel hardware. Our thorough experimental analysis demonstrates the advantage of our novel partitioning-based approach for parallel computation.

Transparent optimisation of rewriting combinators

1999 ◽  
Vol 9 (2) ◽  
pp. 113-146 ◽  
Author(s):  
RICHARD J. BOULTON
Keyword(s):  
Data Structure ◽  
Functional Programming ◽  
Engine Performance ◽  
Theorem Prover ◽  
Modular Approach ◽  
Standard Ml ◽  
System A ◽  
Comparative Results ◽  
Higher Order Functions ◽  
Mechanical Theorem

The LCF system was the first mechanical theorem prover to be user-programmable via a metalanguage, ML, from which the functional programming language Standard ML has been developed. Paulson has demonstrated how a modular rewriting engine can be implemented in LCF. This provides both clarity and flexibility. This paper shows that the same modular approach (using higher-order functions) allows transparent optimisation of the rewriting engine; performance can be improved while few, if any, changes are required to code written using these functions. The techniques described have been implemented in the HOL system, a descendant of LCF, and some are now in daily use. Comparative results are given. Some of the techniques described, in particular ones to avoid processing parts of a data structure that do not need to be changed, may be of more general use in functional programming and beyond.

scholarly journals Semi-automatic exploratory data analytics for actionable discoveries through subgroup mining

2017 ◽  
Author(s):  
◽  
Danlu Liu
Keyword(s):  
Real Data ◽  
Search Space ◽  
Complex Data ◽  
New Paradigm ◽  
Quantitative Measurements ◽  
Exploratory Data ◽  
Index Contrast ◽  
Exploratory Data Mining ◽  
Contrast Measurement ◽  
Complex Data Structure

People are born with the curiosity to see differences between groups. These differences are useful for understanding the root causes of certain discrepancies, such as populations and diseases. However, without prior knowledge of the data, it is extremely challenging to identify which groups differ most, let alone to discover what associations contribute to the differences. The challenges are mainly from the large searching space with complex data structure, as well as the lack of efficient quantitative measurements that are closely related to the meaning the differences. To tackle these issues, we developed a novel exploratory data mining method to identify ranked subgroups that are highly contrasted for further in-depth analyses. The underpinning components of this method include (1) a semi-greedy forward floating selection algorithm to reduce the search space, (2) a deep-exploring approach to aggregate a collection of sizable and creditable candidate feature sets for subgroups identification using in-memory computing techniques, (3) a G-index contrast measurement to guide the exploratory process and to evaluate the patterns of subgroup pairs, and (4) a ranking method to provide mined results from highly contrasted subgroups. Computational experiments were conducted on both synthesized and real data. The algorithm performed adequately in recognizing known subgroups and discovering new and unexpected subgroups. This exploratory data analysis method will provide a new paradigm to select data-driven hypotheses that will produce potentially successful actionable outcomes to tailor to subpopulations of individuals, such as consumers in E-commerce and patients in clinical trials.

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